Image information is more helpful for PERS: we observe an increase of 10% in resolved blanks for del+obj as compared to del.

for PERS the textual context is still enough in the majority of the cases: models tend to associate men with sports or women with cooking and are usually right (see Figure 4 example (c)).


(2017) optimise image description generation to capture both semantic faithfulness and syntactic fluency by combining metrics with complementary properties.

the combination weights need to be engineered towards the task.


Our proposal is similar to CIDEr in that we capture consensus information given a set of human references.

we significantly differ in that (i) we use the references to explicitly model object importance, instead of directly comparing the candidates against the references; (ii) we perform word matching in a semantic space using word embeddings rather than surface forms.


Human language is often multimodal, which comprehends a mixture of natural language, facial gestures, and acoustic behaviors.

two major challenges in modeling such multimodal human language time-series data exist: 1) inherent data non-alignment due to variable sampling rates for the sequences from each modality; and 2) long-range dependencies between elements across modalities.


(2018a) proposed a joint framework for generating reference reports and performing disease classification at the same time.

this method was based on a single-sentence generation model (Xu et al., 2015), and obtained low BLEU scores.


Hsu has observed that people often replace "determiner/article + noun" phrases (e.g., "a boy") with pronouns (e.g., "he") in AREL stories (2019).

this observation cannot explain the story lengthening in GLAC, where each story on average has an increased 0.9 nouns after editing.


(2016), which suggests that METEOR could be useful when comparing among stories generated by the same visual storytelling model.

when comparing among machine-edited stories (row y and |), among pre-and post-edited stories (row z and }), or among any combinations of them (row~, and ), all metrics result in weak correlations with human judgments.


Recognizing relationships between entities is non-trivial because the space of possible relationships is immense, and because there are O(n 2 ) relationships possible when n objects are present in an image.

while image captions are easier to obtain, they are often not ⇤ Equal Contribution completely descriptive of an image (Krishna et al., 2017).


If there are no confluence states in the common prefix, then the method of adding the rest of the word does not differ from the method used in the algorithm for sorted data.

we need to withdraw (from the register) the last state in the common prefix path in order not to create cycles.


For the sake of presentational convenience, the above describes a construction working on the complete grammar.

our implementation applies the construction separately for each nonterminal in a set Ni such that recursive(Ni) = self, which leads to a separate subautomaton of the compact representation (Section 3).


The operation of a traditional left-to-right transducer can be simulated by a head transducer by starting at the leftmost input symbol and setting the positions of the first transition taken to a = 0 and fl = 0, and the positions for subsequent transitions to o~ = 1 and fl = 1.

we can illustrate the fact that head transducers are more expressive than left-to-right transducers by the case of a finite-state head transducer that reverses a string of arbitrary length.


The definition of head transducers as such does not constrain these.

for a dependency transduction model to be a statistical model for generating pairs of strings, we assign transition weights that are derived from conditional probabilities.


This results in bitexts in which the number of multicharacter Japanese "words" is at most the number of English words.

as noted above, evaluation of the Japanese output is done with Japanese characters, i.e., with the Japanese text in its natural format.


Instead of trying to improve upon determinization techniques for such automata, it might be more fruitful to try to improve these approximation techniques in such a way that more compact automata are produced.

1 because research into finite-state approximation is still of an exploratory and experimental nature, it can be argued that more robust determinization algorithms do still have a role to play: it can be expected that approximation techniques are much easier to define and implement if the resulting automaton is allowed to be nondeterministic and to contain c-moves.


No consensus has been reached on the matter to the best of the author's knowledge.

our proposed n-tape machines move all the read heads simultaneously ensuring finite-state expressive power.


Since these two operations are very similar, one would not expect much of a difference if the elements in each sublexicon were the same.

the different formal renderings of roots and patterns in the two approaches results in a significant difference.


Since the planner doesn't know which alternative(s) is/are available, it can't choose between them; the linguistic component must make the choice.

the decision has to be made by the planner, since it can depend on and/or affect the goals the generator is trying to achieve.


IGEN in some ways resembles a blackboard architecture (Nii 1986b(Nii , 1986a; like a blackboard, its workspace contains several possible utterances that are described at different levels of structure and are built up incrementally by various independent components.

it doesn't have the strict hierarchical structure that usually characterizes blackboard systems; each request from the planner may correspond to several linguistic options, and the linguistic options may handle (parts of) more than one request.


In general, graphical objects, functioning as constant terms or as variables, introduced as antecedents or as pronouns, cannot be expressed in a DRS, since the rules constructing these structures are triggered by specific syntactic configurations of the natural language in which the information is expressed.

this limitation can be overcome if graphical information can be expressed in a language with well-defined syntax and semantics.


Performing a multimodal reasoning process is possible if the translation relation between expressions of different modalities is available.

for particular multimodal reasoning tasks, the translation relation between individual constants of different modalities cannot be stated beforehand and has to be worked out dynamically through a deictic inferential process, as will be argued in the rest of this paper.


Similarly, the expression inside(,~P3y[region(y)/~ P(y)])(z) denotes that the dot z is inside a region y, but the first argument denotes the set of properties P that the region has, rather than denoting y directly.

whenever the full interpretation of these expressions in relation to a finite domain of graphical objects is required, they must be transformed into equivalent first-order expressions.


Other examples are expressions of the form &P[P(el)  where ei is an individual constant, which denote the set of properties that one or another individual has.

this latter kind of expression could be translated if the expressiveness of L were augmented by allowing conjoined term phrases in the grammar.


Consider, for instance, reading a book with words and pictures: when the associations between textual and graphical symbols are realized by the reader, the message as a whole has been properly understood.

it cannot be expected that such an association can be known in advance.


Consider that linguistic terms serve to identify individuals, and whenever they are used, the individual they denote should exist.

as pointed out by Donnellan and commented on by Kaplan, "using a definite description referentially a speaker may say something true even though the description correctly applies to nothing" (Kaplan 1978).


There may be constants of P that cannot be translated into L as proper names.

consider, for instance, the line 11 in Figure 4; the translation of this line into G is &P[P(I1)], but as can be seen in Figure 14, there is no proper name that corresponds to this expression in L. this constant could be translated by rule TIp_G (b) as the denoting concepts A/THE(line).


in certain cases it is useful to include as much information as possible given the size limit; for example, I want to convey as much information as possible about my research in the allowed eight pages.


Furthermore, it is important to communicate as much information as possible subject to the size constraint; this is a characteristic that the system tries to optimize.

it is even more important that leaflets be easy to read, and size optimization should not be at the expense of readability.


if "bad for your health" in the paragraph just below the graphic were changed from italic face to bold face, then this paragraph would require four lines instead of three lines.


The best results of all are in revision mode, although the increase in size over a four-solution pipeline (385 words versus 380 words) is small.

revision mode also is robust in the face of increased data set size (we can be confident that the size constraint will be satisfied even on a set of a million questionnaires) and "last-minute" changes to the code.


As expected, processing time is lowest for the single-solution pipeline and highest for revision mode.

in the context of STOP, even the 9.8 seconds required in revision mode is acceptable; under this mode a batch of 100 leaflets can still be generated in under 20 minutes.


Revision mode does better than the multiple-solution pipeline, but only slightly.

revision mode is robust in the face of increased data set size and changes to the code.


For example, in a speech system a word-level analysis component may pass several word hypotheses to a language model; and in a natural language analysis system, a morphology system may pass several possible analyses of a surface form word to a parser.

multiple-solution pipelines have not received a great deal of attention in the NLG community.


For example, an important constraint in STOP is that texts should be easy to read for poor readers.

the only computational mechanism we are aware of for measuring reading difficulty is reading-level formulas (such as Flesch Reading Ease), whose accuracy is doubtful (Kintsch and Vipond 1979).


The authors write further, "A valid alternative analysis may involve a structural topic position for sentential subjects.

... the development of such an alternative analysis presupposes a large amount of linguistic research outside the scope of the Par-Gram collaboration" (p. 99).


The second shows the leading paragraphs of newspaper articles being better than automatic summaries.

the third illustrates genuine utility from summarization.


Natural language constructions can require even more complex forms such as anbnc n, which in turn must be defined using indexed grammars and parsed with a machine that has the equivalent of multiple stacks of memory.

"suppose that we had access to hardware that would handle FSTNs [finite-state transition networks] ... ultrafast and observed that in actual occurrences of anb n constructions, the value of n never exceeded 3; then we might decide to compile our RTN [recursive transition network] ... descriptions down into FSTNs subject to an n = 3 upper bound (such a compilation is possible for any given finite upper bound on the value of n)" (Gazdar and Mellish 1989).


After identifying some fundamental problems in SFG theory, the author explains that these problems have been fully solved in HPSG.

rather than abandoning SFG and adopting HPSG, the author prefers to try to integrate the HPSG-style solutions into the existing SFG theory.


For both statistical tests, recall seems to be optimal at window size 2.

at this window size, the number of words extracted is very small.


The numbers above the panels indicate the A-B distribution of the contingency tables in Table 2. log-likelihood ratio because the extreme saw-tooth-shaped pattern is substantially reduced.

the use of Fisher's exact test does not eliminate the effect of the choice of window and complement size on the number of significant words and recall.


Extracting such particle-verb combinations is relatively straightforward.

when the particle follows the verb, it may be separated from the verb by many constituents of arbitrary complexity: Hij zegt de belangrijke afspraak met de programmeur voor vanmiddag af ('he says the important meeting with the programmer for this afternoon off'; i.e., he cancels the meeting).


Both sentences exhibit both quantifier scopings: a.

~xVy(love y x) b. Vy3x(love y x) while the dominant reading of (7a) is (8a), that of (7b) is (8b), i.e., the preference is for the first quantifier to have wider scope.


Each sequent has a distinguished category formula (underlined) on which rule applications are keyed: In the regulated calculus there is no spurious ambiguity, and provided there is no explicit or implicit antecedent product, i.e., provided .L is not needed, F ~ A is a theorem of the Lambek calculus iff F ~ A is a theorem of the regulated calculus.

apart from the issue regarding .L, there is a general cause for dissatisfaction with this approach: it assumes the initial presence of the entire sequent to be proved, i.e., it is in principle nonincremental; on the other hand, allowing incrementality on the basis of Cut would reinstate with a vengeance the problem of spurious ambiguity, for then what are to be the Cut formulas?


3 For zeroth-order (unigram) discourse grammars, Viterbi decoding and forwardbackward decoding necessarily yield the same results.

for higher-order discourse grammars we found that forward-backward decoding consistently gives slightly (up to 1% absolute) better accuracies, as expected.


Our paradigm thus follows historical practice in the Switchboard domain, where the goal is typically the off-line processing (e.g., automatic transcription, speaker identification, indexing, archival) of entire previously recorded conversations.

the HMM formulation used here also supports computing posterior DA probabilities based on partial evidence, e.g., using only the utterances preceding the current one, as would be required for on-line processing.


As expected, we see an improvement (decreasing perplexities) for increasing n-gram order.

the incremental gain of a trigram is small, and higher-order models did not prove useful.


The first alternative approach was a standard cache model (Kuhn and de Mori 1990), which boosts the probabilities of previously observed unigrams and bigrams, on the theory that tokens tend to repeat themselves over longer distances.

this does not seem to be true for DA sequences in our corpus, as the cache model showed no improvement over the standard N-gram.


As we have shown here, such models offer some fundamental advantages, such as modularity and composability (e.g., of discourse grammars with DA models) and the ability to deal with noisy input (e.g., from a speech recognizer) in a principled way.

many other classifier architectures are applicable to the tasks discussed, in particular to DA classification.


Finally, we developed a principled way of incorporating DA modeling into the probability model of a continuous speech recognizer, by constraining word hypotheses using the discourse context.

the approach gives only a small reduction in word error on our corpus, which can be attributed to a preponderance of a single dialogue act type (statements).


For example, if the character b were to follow tobeornottobe it would be encoded with probabilities (1/2,1/2, 3/26), without exclusion, leading to a coding requirement of 5.1 bits.

if exclusion was exploited, both encoder and decoder will recognize that escape from order 1 to order 0 is inevitable because the order 1 model adds no characters that were not already predicted by the order 2 model.


For example, both results in the first row are incorrect: no space should be inserted in this case, and the four characters should stand together.

the order 3 result is to be preferred to the order 5 result because both two-character words do at least make sense individually, whereas the initial three characters in the order 5 version do not represent a word at all.


Of course, full-text indexes can be built from individual characters rather than words.

these will suffer from the problem of low precision--searches will return many irrelevant documents, where the same characters are used in contexts different from that of the query.


The word -~ ("also") is another frequent character, appearing 4,553 times in the PH corpus.

in 4,481 of those cases it appears by itself and contributes little to the meaning of the text.


Despite their formal elegance, implementations of these theories cannot yet handle naturally occurring texts, such as that shown in (1).

the theories aimed at characterizing the constraints that pertain to the structure of unrestricted texts and the computational mechanisms that would enable the derivation of these structures (van Dijk 1972;Zock 1985;Grosz and Sidner 1986;Mann and Thompson 1988;Polanyi 1988Polanyi , 1996Hobbs 1990) are either too informal or incompletely specified to support a fully automatic approach to discourse analysis.


The best that we can do in this case is to make an exclusively disjunctive hypothesis, i.e., to hypothesize that one and only one of these possible relations holds.

it is still unclear what the elements of such an exclusively disjunctive hypothesis should be.


Let us focus our attention again on text (4).

we have seen that a computer program may be able to hypothesize the first exclusive disjunction in (6) using only knowledge about the discourse function of the connective .


After all, one can use the annotated data to derive such information automatically.

during my prestudy of cue phrases, I noticed that there is a finite number of ways in which cue phrases can be used to identify the elementary units of text.


In fact, some psycholinguistic and empirical research of Heurley (1997) and Hearst (1997) indicates that paragraph breaks do not always occur at the same locations as the thematic boundaries.

experiments of Bruder and Wiebe (1990) and Wiebe (1994) show that paragraph breaks help readers to interpret private-state sentences in narratives, i.e., sentences about psychological states such as wanting and perceptual states such as seeing.


For example, if the cue phrase Besides occurs at the beginning of a sentence and is not followed by a comma, as in text 9, it usually signals a rhetorical relation that holds between the clause-like unit that contains it and the following clause(s).

if the same cue phrase occurs at the beginning of a sentence and is immediately followed by a comma, as in text (10), it usually signals a rhetorical relation that holds between the sentence to which Besides belongs and a textual unit that precedes it.


Usually, the discourse role of the cue phrases and and or is ignored because the surface-form algorithm that we propose is unable to distinguish accurately enough between their discourse and sentential usages.

lines 8-9 of the algorithm concern cases in which their discourse function can be unambiguously determined.


It is good news and bad news: people who need to build a simple question-answering system or talking robot could find the suggested approach useful.

the book does not provide any deep linguistic discussion, considering mostly the John loves Mary kind of artificial examples.


Second, the complexity of a grammar class is measured by the worst case: a grammar class has a complexity x if there exists some grammar in this class such that there exists an infinite series of long-enough sentences that parse in time x by this grammar.

what matters in engineering practice is the average case for a specific grammar.


This is due to the lack of a formal definition of style as well as to the inability of current NLP systems to incorporate stylistic theories that require complicated information.

to traditional stylistics based on formal linguistic theories, the use of statistical methods in style processing has proved to be a reliable approach (Biber 1995).


This parser produces trees to represent the structure of the sentences that compose the text.

it is set to "skip" or surrender attempts to parse clauses after reaching a time-out threshold.


Recently, Yang (1999) studied the performance of several classifiers on text categorization tasks and concluded that all the tested methods perform comparably when the training set comprises over 300 instances per category.

when the number of positive training instances per category is small (less than 10) a regressionlike method called linear least-squares fit and k-nearest neighbors outperform neural networks and naive Bayes classifiers (Yang and Liu 1999).


It must also be pointed out that the last three text genres (i.e., G08, G09, and G10) refer to spoken language that has been transcribed either before (i.e., planned speeches, broadcast news) or after (i.e., interviews) it has been uttered.


For group A, there are significant differences in the accuracy of the two techniques.

three authors (A01, A03, and A06) are responsible for approximately 50% of the average error rate, probably because the average text length of these authors is relatively short, i.e., shorter than 800 words (see Table 5).


The performance of VR is not significantly affected by increasing the training set size.

the identification error rate of CWF-30, CWF-50, and that of our approach is generally reduced by increasing the number of texts used for training.


at QLF we represent the semantics of the elliptical sentence not with a free variable but by using the construct vpEIlipsis .......


It does not assign a full meaning to QLF constructs like he or every in isolation, but only in a context.

the coherence of such an approach is dependent on how fine-grained our notion of context is made to be.


(The CLE-QLF supervaluation semantics falls prey to this problem.)

if we know both readings are true, then we can safely assert the ambiguous expression (2).


One advantage of linguistic indicators is that they can be measured automatically.

individual linguistic indicators are predictively incomplete, and are therefore insufficient when used in isolation.


For example, stativity must be identified to detect temporal constraints between clauses connected with when.

for example, in interpreting,  in interpreting, Phototherapy was discontinued when the bilirubin came down to 13. the discontinue event began at the end of the come event: Such models also predict temporal relations between clauses combined with other connectives such as before, after, and until.


this may be due to the limited size of our supervised data.


Note that, as for stativity, this classification performance was attained with a subset of only five linguistic indicators: no subject, frequency, temporal adverb, perfect, and not progressive.

(only two of these appeared in the example function tree for stativity shown in Figure 2: frequency and progressive.)


Such theoretical elegance appears at first examination to benefit efficiency at implementation time, which is what attracts computational linguistics to these approaches.

the difficulties encountered in trying to extend their coverage at the same time delimit the level of their usefulness for system development, while helping the theorists further the instantiation of the paradigms implied by their theories.


The softening of these consonants is indicated by the -i that precedes the canonical Loc.Sg.

ending -e. for our purposes it is more straightforward to consider the Loc.Sg.


Unlike the substitution operation, where an entire tree is inserted below the argument node, with adjunction, only a subtree of ~ appears below the argument node; the remainder appears in its entirety above the root node of ft.

if we view the trees as descriptions, as in Figure 2, and if we take the expansion of the foot node as the main goal served by adjunction, it is not clear why the composition should have anything to say about the domination relationship between the other parts of the two objects being combined.


As we said above, a frontier node of a component of a d-tree can be a d-parent but not an i-parent, and only frontier nodes of a component can serve as d-parents.

by definition, a frontier node of a d-tree can neither be a d-parent nor an i-parent.


Normally, substitution is used in LTAG to associate a complement to its head, and adjunction is used to associate a modifier.

adjunction rather than substitution must be used with complements involving long-distance dependencies, e.g., in wh-dependencies and raising constructions.


(1995) for compiling an HPSG fragment to TAG-like structures.

to traditional TAG analyses (in which the elementary tree contains the preposition and its PP, with the NP complement of the preposition as a substitution node), the PP argument of the ditransitive verb is not expanded.


(We return to analyses using multicomponent TAG in Section 4.2.)

we now show that all of these cases can be captured uniformly with generalized substitution (see Figure 17).


Specifically, there are constructions (which do not involve long-distance phenomena) for which one of the most widely developed and comprehensive theories for determining the nature of localization in elementary trees--that of Frank (1992)---calmot be used because of the nature of the TAG operation of adjunction.

the operations of DSG allow this theory of elementary lexical projections to be used.


As mentioned previously, while we can derive any ordering, we cannot, in DSG, obtain a flat VP structure.

our analysis has an advantage when we consider "long scrambling," in which arguments from two lexical verbs intersperse.


If the special mechanism produces a single (flat) VP for the new argument list, then LP rules for the simplex case can also apply to the complex case.

the DSG analysis has the advantage that it does not involve a special mechanism, and the difference between German and English complex clauses is related simply to the difference in word orders allowable in the simplex case (i.e., German but not English allows scrambling).


Furthermore, like DSG, TDG does not allow the conflation of immediate dominance structure specified in elementary structures.

tDG allows for more than one node to be equated in a derivation step: nodes are "marked" and all marked nodes are required to be equated with other nodes in a derivation step.


The book contains material that will be of value especially to experts in this field.

most of the papers in the volume will also be relevant to researchers from other branches of computational linguistics who are interested in theoretical aspects of the computation of meaning in natural language.


The length of the stems and suffixes in question clearly plays a role: suffixes of one letter are, all other things being equal, suspicious; the pair of stems Ioo and boo, appearing with the signature k.t, does not provide an example of a convincing linguistic pattern.

if the suffix is long enough, even one stem may be enough to motivate a signature, especially if the suffix in question is otherwise quite frequent in the language.


The changes are mainly cosmetic, e.g., nonalphabetic characters such as "$" in tag names have been replaced.

there has also been some retokenization: genitive markers have been split off and the negative marker n't has been reattached.


The final model has a weighting parameter for each feature value that is relevant to the estimation of the probability P(tag I features), and combines the evidence from diverse features in an explicit probability model.

to the other taggers, both known and unknown words are processed by the same 17 Because of the computational complexity, we have had to exclude the system from the experiments with the very large Wotan tagset.


it would seem that the accuracy level at 1M words is a good approximation of the eventual ceiling.


As we have seen, the WotanLite taggers undeniably have a much higher accuracy than the Wotan ones.

this is hardly surprising, as they have a much easier task to perform.


Here, higher granularity on the part of the ingredients is preferable, as combiners based on Wotan taggers perform better than those based on WotanLite taggers, 3° and ingredient performance seems to be even more useful, as BestLite yields yet better results in all cases.

30 this comparison is not perfect, as the combination of Wotan tags does not include TBL.


While this will perhaps result in somewhat artificially inflated scores, there may be no reason to think that it would benefit one system or approach more than another, and this concession might seem reasonable considering the fact that there is likely to be no completely foolproof way to perform alignments.

the potential harm that this behavior manifests in terms of the technology development process--which, to our knowledge, has never been brought to light--is that it creates a situation in which uncontroversially positive changes in system output may result in a dramatically worse score, and likewise negative changes may result in a dramatically better score.


This is the anticipated result, and would constitute a large increase over the zero F-score that the overgenerated event should have received when standing alone.

this is a substantial reduction from the F-score of 0.571 that the overgenerated event actually receives, a change which implicitly instructs the developer to remove the rules responsible for extracting the correct event.


It is difficult to answer this question, of course, since one cannot replay past phases of technology development.

we do have a case study with which to investigate this question, as we have previously performed a 13 1/2-month effort in which we focused on improving performance on the MUC-6 task.


In most cases, the effect was to reduce the score assigned by the standard algorithm, since the best-scoring (albeit incorrect) alignment found by the standard algorithm was often disallowed by the restrictive algorithm.

due to the fact that the heuristic search process used for alignment is less likely to find the optimal mapping with the standard criterion, there were also cases in which the effect was to increase the score.


It is important to note that the same spurious parses also occur in context-free parsing, specifically in Earley's algorithm.

since the only information a constituent carries in context-free grammar is the grammar symbol, the spurious derivations only produce exactly the same results as the normal ones.


These are known techniques, which have been applied to solve other problems in unification-based systems.

most of them only offer partial solutions to the nonminimal derivation problem.


This book is "intended for anyone whose job is to analyze knowledge," and practitioners will find it useful.

it is also designed for the student, and includes an extensive set of exercises at the end of each chapter and answers to selected exercises at the end of the book.


I think that the authors could have addressed this aspect by stressing the fact that the tasks to be accomplished by a generation system are of much greater variety than those needed for natural language understanding: • On the one hand, many aspects of pragmatics, which is one of the least understood areas in linguistics, are more important in generation than in analysis and, therefore, they have to be captured to a certain extent.

• the initial representations for a generation system are much more distant from the language surface than the results of understanding systems, and they can appear in various, mostly nonlinguistic forms.


As for the other topics, it is not surprising that they have not been addressed in the book, given the currently low degree of understanding of these issues.

for progress in system-building techniques, a deeper understanding of these questions will be crucial.


All papers have been revised, and most expanded.

they are not particularly well integrated in this massive work, which combines patches of brilliance with unnecessary wordiness and repetition--it needed a stern editor.


This would not be a fault if the authors were content to present their findings as purely descriptive.

they do venture a few theoretical claims that have little foundation and in fact contradict their own descriptive practice.


Secondly, there is no guarantee that the techniques employed will be applicable when a larger vocabulary is tackled.

it is likely that mark-up for a restricted vocabulary can be carried out more rapidly since the subject has to learn the possible senses of fewer words.


This method would tag all content words in a sentence with their senses from a dictionary that contains textual definitions.

it was found that the computations which would be necessary to test every combination of senses, even for a sentence of modest length, was prohibitive.


implementation this was done using a simple count of the number of words (tokens) in common between all the definitions for a given choice of senses.

this method prefers longer definitions, since they have more words that can contribute to the overlap, and short definitions or definitions by synonym are correspondingly penalized.


Then a corpus with an average polysemy of 5, and 2 senses marked correct on each ambiguous token, will have a baseline not less than 40%.

one with an average polysemy of 2, and only a single sense on each, will have a baseline of at least 50%.


This is because it is common for English words to act as either a noun or a verb and, since these are the most polysemous grammatical categories, the average polysemy count becomes large due to the cumulative effect of polysemy across grammatical categories.

words that can act as adjectives or adverbs are unlikely to be nouns or verbs.


One can legitimately wonder whether in fact the different knowledge sources for WSD are all ways of encoding the same semantic information, in a similar way that one might suspect transformation rules and statistics encode the same information about part-of-speech tag sequences in different formats.

the fact that an optimized combination of our partial taggers yields a significantly higher figure than any one tagger operating independently, shows that they must be orthogonal information sources.


We have already examined the usefulness of part-of-speech tags for semantic disambiguation in Section 3.

we now want to know the effect it has within a system consisting of several disambiguation modules.


It also remains an interesting question whether, because of the undoubted existence of novel senses in text, a sense tagger can ever reach the level that part-of-speech tagging has.

we believe we have shown that interesting combinations of WSD methods on a substantial training corpus are possible, and that this can show, among other things, the relative independence of the types of semantic information expressed by the various forms of lexical input.


1 Although speech lacks explicit demarcation of word boundaries, it is undoubtedly the case that it nevertheless possesses significant other cues for word discovery.

it is still a matter of interest to see exactly how much can be achieved without the incorporation of these other cues; that is, we are interested in the performance of a bare-bones language model.


The maximum likelihood approach due to Olivier (1968) is probabilistic in the sense that it is geared toward explicitly calculating the most probable segmentation of each block of input utterances (see also Batchelder 1997).

the algorithm involves heuristic steps in periodic purging of the lexicon and in the creation in the lexicon of new words.


As expected, the effect of reduced history is apparent from an in- In the trigrarn model, -log P(houselthe, dog) = 4.8 and -log P(doglin, the) = 5.4, giving a score of 10.2 to the segmentation dog house.

due to the error in transcription, the trigram in the doghouse is never encountered in the training data, although the bigram the doghouse is.


Generally, we would expect that causative verbs (in our case, the unergative and unaccusative verbs) would have a greater degree of overlap of nouns in subject and object position than non-causative transitive verbs (in our case, the object-drop verbs).

since the causative is a transitive use, and the transitive use of unergatives is expected to be rare (see above), we do not expect unergatives to exhibit a high degree of detectable overlap in a corpus.


~1 Assessing the percentage of verbs on which the experts agree gives us an intuitive measure.

this measure does not take into account how much the experts agree over the expected agreement by chance.


In fact, the accuracy of 69.5% achieved by the program is only 1.5% less than one of the human experts in comparison to the gold standard.

the algorithm still does not perform at expert level, as indicated by the fact that, for all experts, the lowest agreement score is with the program.


We conclude that the verb classification task is likely easier for very homogeneous classes, and more difficult for more broadly defined classes, even when the exemplars share the critical syntactic behaviors.

frequency does not appear to be a simple factor in explaining patterns of agreement between experts, or increases in accuracy.


We can see here that different frequency groups yield different classification behavior.

the relation is not simple, and it is clearly affected by the composition of the frequency group: the middle group contains mostly unaccusative and objectdrop verbs, which are the verbs with which our experts have the most difficulty.


13 This is a very important property of lexical acquisition algorithms to be used for lexicon organization, as their main interest lies in being applied to unknown words.

our approach is similar to the approaches of Siegel, and Lapata and Brew (1999), in attempting to learn semantic notions from distributions of 12 A baseline of 5% is reported, based on a closest-neighbor pairing of verbs, but it is not straightforward to compare this task to the proposed clustering algorithm.


Like Lapata and Brew, three of our indicators--TRANS, VBN, PASS--are based on the assumption that distributional differences in subcategorization frames are related to underlying verb class distinctions.

we also show that other syntactic indicators--cAUS and ANIM--can be devised that tap directly into the argument structure of a verb.


A header is then the layout equivalent of a discourse marker.

since headers cannot typically function as relationally as discourse markers (because they cannot readily point back to their point of origin), they tend instead to give sufficient information for the reader to make a connection--for example, by summarizing the nucleus of their segments.


This section has set out a procedure for producing layout structures from rhetorical structures.

the mapping remains highly nondeterministic.


This is a little forced, but useful for providing an overview of the lexicon in speech processing.

there is a strong element of Unix hacking, without a comprehensive view of traditional computational lexicology and no reference to computational lexicography as may be practiced by dictionary publishers (e.g., corpus evidence).


The editors have selected an interesting group of papers, and provide a clear introduction with useful summaries of the chapters, pointing out some interesting relationships between the different lines of research.

1 despite the high price of the book, there is no evidence that a competent professional copy editor was involved in the process of publication.


In addition, the book provides a wealth of descriptive information on GCI as well as many pointers to related work in theoretical linguistics.

unfortunately, the book lacks a computational or formal orientation.


At the same time, under the Unicode initiative, there has been solid progress in the definition of and, finally, the implementation of standards for computer encoding and rendering of scripts used around the world.

the implications of this multi-lingual revolution for computational linguistics beyond the level of word-processing have not been well explored, and Richard Sproat's book A Computational Theory of Writing Systems is a welcome contribution.


Beginning with the pioneering work of Hobbs (1978), many practical approaches rely on the availability of syntactic surface structure by employing coindexing restrictions, salience criteria, and parallelism heuristics (e.g., Lappin and Leass 1994).

even the assumption of the availability of a unique syntactic description is unrealistic since, in general, parsing involves the solution of difficult problems like at-tachment ambiguities, role uncertainty, and the instantiation of empty categories.


Clearly, this holds with respect to the candidates der and ihn, which are contained in the same surface structure fragment.

even regarding the two candidates Mann and Prf~sidenten that occur in the other fragment, there is no loss of evidence: since the reflexive pronoun is of binding-theoretic type A, and the fragment in which it occurs contains its binding category (the S node of the 7 this statement applies solely to the direct comparison of the involved occurrences, since in case of further, transitive coindexings, negative evidence stemming from decision interdependency may get lost (cf.


Regarding the errors made during the identification of specifying occurrences, this goal may be met by basing the measures on the sets 0++, 0+_, o+.7, 0+_, and 0+, that constitute the cases in which the base entity to be decided upon, namely, the pronoun occurrence P, has been determined in compliance with the key.

by further drawing the usual distinction between precision and recall, according to which, in the latter case, one must take into account empty anchors A as well, one obtains the following definitions: 34 JOq_q_ I Io++1 + Io+-I + Io+?1 Io++1 + Io+-I + Io+.71 + Io+_l Since errors in the occurrence identification are excluded from measurement at this stage of evaluation, and, moreover, it is assumed that there are no errors regarding the classification of occurrences as decision-relevant entities (i.e., pronouns), 3s it follows that, in any case, P > R. the characteristic trade-off relation between precision and recall holds anyway.


On the Mozart operas corpus, which, in this sense, is easier, the scores are considerably higher.

since different evaluation corpora have been used, and, moreover, since the precision figure mentioned in the results of Kennedy and Boguraev (1996) is not qualified with respect to text genre, a direct comparison of the empirical results should be based on further investigations.


For example, (Metaphor)l should be assigned "organization" but it is assigned "unknown" in (18), and (second-quarter)2 should be assigned "date" instead of "unknown" in (19).

correcting these classes will still not cause the noun phrases in the examples to corefer.


For example, JV-CHILD-i was changed to CHILD-/to decide whether i is a "unit" or a "subsidiary" of a certain parent company.

to RESOLVE, our system makes use of a smaller set of 12 features and, as in Aone and Bennett's (1995) system, the features used are generic and applicable across domains.


Grosz, Joshi, and Weinstein (1995) state that Cf may be ordered using different factors, but they only use information about grammatical roles.

both Strube (1998) and Strube and Hahn (1999) point out that it is difficult to define grammatical roles in free-word-order languages like German or Spanish.


Kessler exhorts everybody who does statistical testing to report all unsuccessful tests together with the successful ones.

he also notes that it is common in the literature on long-range comparisons to present only the most striking results.


If this were the only recently published book that brought together robust language and speech processing in an accessible way, then I would have no hesitation in recommending it as a convenient reference.

the book faces stiff competition from excellent books by Rayner el al.


the publication of this volume does make previously difficult-tofind material more readily available and does provide an interesting historical context for contemporary research.


Accordingly, they have been considered one of the most robust and intriguing grammar submodules, usually referred to as binding theory.

in contrast to this, the formal and computational handling of binding constraints has presented considerable resistance.


Nevertheless, this solution turns out to be dependent on a top-down parsing strategy.

while Ingria and Stallard's implementation is independent of the parsing strategy adopted, its independence comes at the cost of still requiring a special-purpose postgrammatical parsing module for binding.


8 Like other types of constraints on semantic composition, binding constraints impose conditions on the interpretation of certain expressions-anaphors, in the present case-based on syntactic geometry.

this cannot be viewed as implying that they express grammaticality requirements.


Precision is the percentage of boundaries identified by an algorithm that are indeed true boundaries; recall is the percentage of true boundaries that are identified by the algorithm.

precision and recall are problematic for two reasons.


In both cases, the algorithms fail to match any boundary precisely; both receive scores of 0 for precision and recall.

algorithm a-0 is close to correct in almost all cases, whereas algorithm a-1 is entirely off, adding extraneous boundaries and missing important boundaries entirely.


This average increases with segment size, as we will discuss later, and changes if one assumes different distributions of false positives throughout the document.

this does not change the fact that in most cases, false positives are penalized some amount less than false negatives.


This method of penalizing false positives achieves this goal: the closer the algorithm's boundary is to the actual boundary, the less it is penalized.

overpenalizing false negatives to do this is not desirable.


One way to fix the problem of penalizing false negatives more than false positives is to double the false positive penalty (or halve the false negative penalty).

this would undermine the probabilistic nature of the metric.


From the above discussion, we know that WD is more lenient in situations where a false negative and a false positive occur near each other (where "near" means within a distance of k 2 ) than P k is.

p k is more lenient for pure false positives that occur close to boundaries.


The usual way of handling unknown morphemes is to guess all the possible POS tags for an unknown morpheme by checking connectable functional morphemes in the same eojeol (Kang 1993).

2 in this way, it is only possible to guess probable POS tags for a single unknown morpheme when it occurs at the beginning of an eojeol.


Rule schema Acronym description N1FT the tag of the first morpheme (FT) of the next eojeol (N1) P1LT the tag of the last morpheme (LT) of the previous eojeol (P1) N2FT the tag of the first morpheme (FT) of the eojeol after the next one (N2) N3FT the tag of the first morpheme (FT) of the second eojeol after the next one N3  of the statistical morpheme tagger.

designing the error correction rules with knowledge engineering is tedious and error prone.


Most tagging systems for Korean have applied flat, fixed tagsets and have suffered from using varying tagsets in various applications.

pOSTAG's tagsets, based on the over 100 finely differentiated pOS symbols for Korean are hierarchically organized and are flexibly reorganizable according to the application at hand.


and over (pages 330-331), extended from their basic senses that are presumed to be rooted in direct physical perception.

these do not give rise to productive procedures that could reliably generate extended senses for equivalent words in other languages, for instance, or other words denoting physical relations or body parts.


A recent issue of this journal described the state of the art in automatic sense disambiguation (Ide and Véronis 1998), and Senseval system competitions have revealed the immense difficulty of the task (http://www.sle.sharp.co.uk/senseval2).

no significant progress can be made on the computational aspects of polysemy without serious advances in theoretical issues.


In Chapter 2, "Aspects of the Micro-Structure of Word Meanings," D. Alan Cruse addresses the issue of the extreme context-sensitivity of word meaning, which can result in an almost infinite subdivision of senses.

cruse believes that there are "regions of higher semantic density" within this extreme variability, which he calls sense-nodules, "lumps of meaning with greater or lesser stability under contextual change."


The authors show that local context cues are very precise when they occur, but often simply do not occur.

topical context is very efficient in helping discriminate between homographs, but not very helpful for identifying the different senses of a polysemous word.


This resource should prove useful for researchers faced with these same decisions during corpus analysis.

the level of detail will sometimes overwhelm computational and corpus linguists attempting to develop automatic taggers and parsers.


For example, Flagstaff -my home town-is tagged as a common noun (page 87), because the word originally referred to a flagpole.

greenwood is tagged as a proper noun (page 88), because it was originally the surname of a colonist.


Here, issues of syntactic and semantic style are involved, as one can choose how the semantic content is to be incorporated.

the latter type of decision involves options that might have subtle semantic and stylistic differences but result in the same syntactic structure (though collocational and subcategorization structure can vary).


Dunning (1993) argues for the use of G 2 rather than X 2 , based on the claim that the sampling distribution of G 2 approaches the true chi-square distribution quicker than the sampling distribution of X 2 .

agresti (1996, page 34) makes the opposite claim: "The sampling distributions of X 2 and G 2 get closer to chi-squared as the sample size n increases.


On the one hand it is clear that people are sensitive to certain kinds of probabilistic information in building interpretations of ambiguous linguistic input and even in generating predictions of what is to come next (and recovering material that was unclear in the original, noisy input signal).

people clearly demonstrate the ability to apply rich and productive linguistic principles in dealing with complex and novel input.


If we know that a capitalized word that follows a period is a common word, we can safely assign such period as sentence terminal.

if we know that a period is not sentence terminal, then we can conclude that the following capitalized word is a proper name.


As one might predict, the most frequent sentence-starting common word was The.

this list also included some adverbs, such as Suddenly, and Once; some prepositions, such as In, to, and By; and even a few verbs: Let, Have, Do, etc.


Having established that, we can apply this information to nonevident contexts and classify Kong as a regular word throughout the document.

if we detect a context such as Kong., said, this indicates that in this document, Kong is normally written with a trailing period and hence is an abbreviation.


When we studied the distribution of capitalized words after capitalized abbreviations, we uncovered an interesting empirical fact.

a capitalized word that follows a capitalized abbreviation is almost certainly a proper name unless it is listed in the list of frequent sentence-starting common words (i.e., it is not The, etc.).


Both POS taggers and named-entity recognizers are normally built using the localcontext paradigm.

we opted for a method that relies on the entire distribution of a word in a document.


14 Class construction is then combinatorially very demanding and depends on frequency counts for joint events involving particular words, a potentially unreliable source of information as we noted above.

41 this is not very satisfactory because one of the goals of our work is precisely to avoid the problems of data sparseness by grouping words into classes.


"Early MT in France," by Maurice Gross, presents only a very brief sketch of the Paris project, which focused on Russian-French translation and terminated early in 1963 after Sestier became convinced that the task was too difficult to pursue further.

christian Boitet's article, "Bernard Vauquois' contribution to the Theory and Practice of Building MT Systems: A Historical Perspective," provides a relatively detailed picture of both the Grenoble project and the accomplishments of its leader in his various roles as researcher, teacher, MT system builder, and international figure in computational linguistics.


Researchers will be more interested in chapters 3, 5, and 6, whereas system designers will probably prefer chapters 7, 9, and 11.

chapters can be read in virtually any order (except for chapter 1, which should be read first, and chapter 12, which assumes prior reading of chapter 8).


This is not surprising, since we can see from Figure 2 that the peripheral vowels in the two vowel inventories are almost the same in the F 1 -F 2 plane.

for the six-vowel system produced in combination with any of the three fitness functions and the seven-vowel systems produced in combination with the first fitness function (F 1F ), there are some big differences between the two vowel inventories.


The bilingual sentences used in training are correct transcriptions of spoken dialogues.

they include spontaneous speech effects such as hesitations, false starts, and ungrammatical phrases.


Systems such as the SRI Core Language Engine (Moran 1988;Moran and Pereira 1992), LUNAR (Woods 1986), and TEAM (Martin, Appelt, and Pereira 1986) have employed scope critics that use heuristics to decide between alternative scopings.

the rules that these systems use in making quantifier scope decisions are motivated only by the researchers' intuitions, and no empirical results have been published regarding their accuracy.


Since the efficiency of the beam search approach is based on restrictions on the allowed coverage vectors C alone, the approach may be used for different types of translation models as well (e.g., for the multiword-based translation model proposed in Och, Tillmann, and Ney [1999]).

since the decoding problem for the IBM-4 translation model is provably NP-complete, as shown in Knight (1999) and Germann et al.


Therefore, the grammar coverage is broad, and it is feasible to use these methods in serious applications.

the translation task was evaluated only in the domain of air travel reservations.


Text-tospeech translation is an inevitable part of a speech-to-speech translation system.

text-to-speech technology is not considered in this book, and prosody translation and prosody transfer are described only briefly.


The trigger his induces the presupposition that a male individual has an apartment.

it does not presuppose that just any male person has an apartment, nor that some boxer or other creature owns an apartment.


Two operations on DRSs used in BAT are merge reduction and presuppositional binding, 2 and both require a precise definition of free and bound variables.

sentence-DRSs allow "ambiguous" bindings.


If we prove that ∀w((w,A) fo →(w,B) fo ), we know that B is not informative with respect to A.

if we are able to show that both ∃w((w,A) fo ∧ (w,B) fo ) and ∃w((w,A) fo ∧ ¬(w,B) fo ) are satisfiable formulas, we can say that B is informative with respect to A.


And just as prosody undoubtedly contributes to the meaning of utterances, so too does a text's graphical presentation contribute to its meaning.

although there is a long tradition and rich linguistic framework for describing and representing speech prosody (e.g., Halliday 1967;Chomsky and Halle 1968;Crystal 1969;Bolinger 1972;Pierrehumbert 1980;'t Hart, Collier, and Cohen 1990;Ladd 1996), the same is not true for text layout.


Mann and Thompson assert this explicitly (1987, page 4): Relations are defined to hold between two non-overlapping text spans, here called the nucleus and the satellite, denoted by N and S. where A text span is an uninterrupted linear sequence of text.

it is unclear whether their claim is intentional or simply the result of an informal style of presentation.


We have already seen this in the case of complex conditionals in example (12).

the phenomenon is much more widespread: In rare cases the treatment can be prolonged for another week; however, this is risky since • The side-effects are likely to get worse.


On first tackling indented structures, we followed precisely this reasoning, introducing vertical list as a text category.

despite its initial plausibility, this decision has several irritating consequences.


Coordinating Conjunction: We will assume that a coordinating conjunction like but may link spans within a text-clause, or across text-clauses and textsentences, so that no constraint on the levels of A and B results.

the satellite must precede the nucleus, so we have position(B) = P 1 (and hence, by Sister Position, position(A) = P 2 ).


To analyze such an example informally may be a useful source of insights, but to attempt a complete formal analysis (and generation) of the page seems bold in the extreme.

despite this difference in approach, the framework that emerges from Bateman et al.


Version 15 (1) Elixir contains gestodene.

elixir is safe to use since • the medicine has been thoroughly tested • it has no significant side effects.


Version 17 (1) Elixir contains gestodene.

the medicine has been thoroughly tested and it has no significant side effects so Elixir is safe to use.


Version 33 (2) Elixir contains gestodene.

since • the medicine has been thoroughly tested • it has no significant side effects Elixir is safe to use.


Version 47 (3) Elixir contains gestodene.

the medicine has been thoroughly tested.


Although conceptually elegant, Pustejovsky's (1995) theory of the generative lexicon does not aim to provide an exhaustive description of the telic roles that a given noun may have.

these roles are crucial for interpreting verb-noun and adjective-noun metonymies.


However, these roles are crucial for interpreting verb-noun and adjective-noun metonymies.

to Vendler (1968), who acknowledges that logical metonymies may trigger more than one interpretation (in other words, that there may be more than one possible event associated with the noun in question), Pustejovsky implicitly assumes that nouns or noun classes have one (perhaps default) telic role without, however, systematically investigating the relative degree of ambiguity of the various cases of logical metonymy (e.g., the out-of-context possible readings for fast scientist suggest that fast scientist exhibits a higher degree of semantic ambiguity than fast plane).


We evaluate our results by comparing the model's predictions to human judgments and show that the model's ranking of meanings correlates reliably with human intuitions.


Furthermore, applying frequency cutoffs reduces the range of the obtained probabilities: only likely (but not necessarily plausible) interpretations are obtained with f (o, e) ≥ 4 and f (v, e) ≥ 4.

one of the aims of the experiment outlined below was to explore the quality of interpretations with varied probabilities.


The result indicates that our model is particularly good at deriving meanings corresponding to the argument bias for a given adjective.

the dispreferred interpretations also correlate significantly with human judgments, which suggests that the model derives plausible interpretations even in cases in which the argument bias is overridden.


Currently our models cannot estimate probabilities for word combinations unseen in the corpus, and WordNet could be used for re-creating the frequencies of these combinations (Lapata, Keller, and McDonald 2001;Clark and Weir 2001).

part of our aim here was to investigate whether it is at all possible to generate interpretations for metonymic constructions without the use of prior knowledge bases that might bias the acquisition process in uncontrolled and idiosyncratic ways.


For example, a fast plane is not only a plane (i.e., an aircraft) that flies, lands, or travels quickly, but also a plane (i.e., a surface) that transposes or rotates quickly and a plane (i.e., a tool) that smoothes something quickly.

more paraphrases are derived for the "aircraft" sense of plane; these paraphrases also receive a higher ranking.


This is not surprising, since the number of verbs related to the "aircraft" sense of plane are more frequent than the verbs related to the other two senses.

to fast plane, however, efficient plane should probably bias toward the "tool" sense of plane, even though the "aircraft" sense is more frequent in the corpus.


In such a critical and knowledge-rich environment, terms cannot be automatically added to the terminology as a consequence of language processing.

cimino describes how simple language processing, together with knowledge-based reasoning, can be used to guide a terminologist in the process of, for example, adding the name of a new drug.


One hundred million words is a large enough corpus for many empirical strategies for learning about language, either for linguists and lexicographers (Baker, Fillmore, and Lowe 1998; Kilgarriff and Rundell 2002) or for technologies that need quantitative information about the behavior of words as input (most notably parsers [Briscoe and Carroll 1997;Korhonen 2000]).

for some purposes, it is not large enough.


The largest appear to have been obtained by Chen and Nie (2000), who acquired collections on the order of 15,000 document pairs for English-French, English-German, English-Dutch, English-Italian, and English-Chinese using the PT-Miner system.

these collections have not been made available to the general community.


Until recently the difficulty with this solution has been that the collection of URLs deteriorates over time as sites disappear, pages are reorganized, and underlying content changes: For example, in April 2002, we attempted to download the documents in the STRAND English-French, English-Chinese, and English-Basque collections, and we were able to access successfully only around 67%, 43%, and 40% of the URL pairs, respectively.

the Internet Archive's Wayback Machine provides a way to distribute persistent URLs.


MT systems try to translate text into a well-readable form governed by morphological, syntactic, and semantic constraints.

current IR models are based on bag-of-words models.


For example, a user might formulate the query "Internet connection" in order to retrieve documents about computer networks, Internet service providers, or proxies.

under the current bag-of-words approach, the relevant documents containing these terms are unlikely to be retrieved.


Several other research groups (for example, the RALI lab at Université de Montréal) have also tried to acquire manually constructed parallel corpora.

manual collection of large corpora is a tedious task that is timeand resource-consuming.


When CLIR is considered simply as a combination of separate MT and IR components, the embedding of the two functions is not a problem.

as we explained in Section 1, there are theoretical motivations for embedding translation into the retrieval model.


This may lead to more reliable estimates for P(e | f ) and P(e | i) than the reverse.

further investigation is needed to confirm this, since differences in morphology could also contribute to the observed effect.


We noted above that faced with the new input in (2), a TM system might be able to present the translator with the fuzzy matches in (1).

if a translator were to set the level of fuzzy matching at 80% (a not unreasonable level), then neither of the translation pairs in (1) would be deemed to be a suitably good fuzzy match, as only 7/9 (77%) of the words in (1a) match those in (2) exactly, and only 3/9 (33%) of the words in (1b) match those in (2) exactly.


You can attach =⇒ Vous pouvez rélier (from (6a)) b. a mouse =⇒ une souris (from (6b)) c. to the connector =⇒ au connecteur (from (6a)) Recombining the French chunks gives us the correct translation in (9): (9) Vous pouvez rélier une souris au connecteur.

a number of mistranslations could also ensue, including those in (10): (10) a.


It may appear, therefore, that there are three chunks in the English string and only two on the French side, but this is not the case: The restriction that each segment must contain at least one non-marker word ensures that we have just two marker chunks for the English string in (18).

it remains the case that the chunks are tagged differently; we obtain the marker chunks in (19): (19) English: <DET> The man looks <PREP> at <DET> the woman French: <DET> L' homme regarde <DET> la femme Our alignment method would therefore align the first English chunk with the first French chunk, as their marker categories match.


A similar effect is seen where the databases of wEBMT were seeded with third-person singular verbs, of course.

we should expect an improvement in translation quality when both sets of verb forms are included in the memories of the system (see Experiment 2).


The major reason that translations fail to be produced in 6% of cases is the absence of a relevant generalized template.

for example, the unseen input her negative TV ads is generalized to (42): 42<POSS> negative TV ads the nearest relevant generalized template found in the system's memory is (43): That is, the template in (43) allows the insertion of any determiner, but no other marker word.


evaluated: Of course, if we consider (say) three-chunk combinations from either system A or B, the only possibilities are AAA or BBB, respectively.

the number of translations produced by the system is less significant than their quality.


With respect to system A, we can assume that our net gain would increase further.

we provide two other interpretations of the net gain of EBMT, calculated using the formula in (47): 47Net Gain = Coverage Percentage + K(Translation Quality) The term Translation Quality in (47) refers to the number of translations preferred by the human evaluator, excluding cases in which one system failed to produce a translation, which is already factored into the equation under the term Coverage Percentage.


the similar approach that we have taken with respect to (40), for instance).

in all of these cases this utility is lessened considerably given that either system B or system C produces a better translation.


The system simply attaches the translation with the highest weight to the existing chunk ordinateurs personnels to produce the mistranslation in (50): (50) *la ordinateurs personnels The problem of boundary friction is clearly visible here: We have inserted a feminine singular determiner into a chunk that was generalized from a masculine plural NP.

rather than output this wrong translation directly, we use a post hoc validation and (if required) correction process based on Grefenstette (1999).


This gives us the probabilities in (52): That is, the string empire est au is about 2.4 times more likely than the string empire sont au.

the count for the second bigram in each example in (52) can of course be discounted, as the juxtaposition of sont or est with au bears no relevance to the correctness or otherwise of the translations in (51).


Also, it is not a balanced corpus, as it contains material from only one genre, namely, news text.

the text originates from a variety of sources (Los Angeles Times, Washington Post, New York Times News Syndicate, Reuters News Service, and Wall Street Journal).


Table 3 shows a subset of the dependencies MINIPAR outputs for the sentence The fat cat ate the door mat.

to Gsearch and Cass, MINIPAR produces all possible parses for a given sentence.


Our goal was to demonstrate that frequencies retrieved from the Web are a viable alternative to conventional smoothing methods when data are sparse; we do not claim that our Web-based method is necessarily superior to smoothing or that it should be generally preferred over smoothing methods.

the next section will present a small-scale study that compares the performance of several smoothing techniques with the performance of Web counts on a standard task from the literature.


The joint probability model performs worse than the conditional model, at 81.1%.

this is still significantly better than the best result of Clark and Weir (2002) (χ 2 (1) = 63.14, p < .01).


The chapters by Worden, Batali, Kirby, and Hurford (see Table 1), whom I shall refer to collectively as WBKH, use computer simulation to demonstrate that agents with no innate syntactic structure can interact to create and preserve both the lexicon and syntax of languages over many generations.

the chapters by Niyogi, Turkel, and Briscoe (collectively, NTB) all base their simulations on an innate Universal Grammar-the general form of grammar is built into the agents, and the lexicon is given little or no importance.


Such structures might seem advantageous in allowing the semantics of the example to be computed directly by compositional rules and defeasible inference.

both structures are directed acyclic graphs (DAGs), with acyclicity the only constraint on what nodes can be connected.


Again, we have abstracted over the individual, as the presupposed defeasible rule associated with the present-tense sentence appears to be more general than a statement about a particular individual.

15 in the following example illustrating a presupposed defeasible rule and a discourse relation associated with adjacency, it seems possible for the presupposed defeasible rule to be about John himself: (57) John is discussing politics.


For example, suppose you needed some money: You'd only have to ask him for it.


(A more precise interpretation would distinguish between the direct and epistemic causality senses of because, but the derivation would proceed in the same way.)

with (66a), example (66b) employs an auxiliary tree (β:punct1) anchored by a period (Figure 13).


A binary-branching (Chomsky adjunction) grammar can generate an unlimited number of adjuncts with very few rules.

for example, the following grammar generates any sequence VP → V NP PP*: the Penn Treebank style would create a new rule for each number of PPs seen in training data.


Similar explanations can be given for other classification decisions.

mB uses only the instances in A and B to construct a classifier.


This is clearly a simplification, since one would expect F(c, f , v) to vary for different verb classes.

note that according to this estimation, F(f , c) will vary across frames reflecting differences in the likelihood of a class being attested with a certain frame.


One might expect an accuracy of 100%, since these verbs can be disambiguated solely on the basis of their frame.

the performance of our model achieves a lesser accuracy, mainly because of the way we estimate the terms P(c) and P(f |c): We overemphasize the importance of class information without taking into account how individual verbs distribute across classes.


Four types of alternations were investigated in this study.

levin lists 79 alternations and approximately 200 classes.


The models proposed by Chao and Dyer (2000) and Ciaramita and Johnson (2000) are not directly applicable to Levin's classification, as the latter is not a hierarchy (and therefore not a DAG) and cannot be straightforwardly mapped into a Bayesian network.

in agreement with Chao and Dyer and Ciaramita and Johnson, we show that prior knowledge about class preferences improves word sense disambiguation performance.


As it is very hard to find all of the words in the original corpus that would be found meaningful by a Chinese person, it is very hard to count recall in the traditional way, that is, the number of meaningful words extracted divided by the number of all meaningful words in the original data.

it is also impossible to approach traditional precision and traditional recall by comparing the hand-segmented sample sentences and the automatically segmented sentences, as people usually do, because our method does not touch upon segmentation.


The performance trends that were observed in Table 2 can be also observed here.

as this corpus is much larger than the previous one, many characters have the chance to occur together to form spurious words.


if we list too many characters as adhesive characters, the partial recall will be degraded.


The two coders still disagree on 25 occurrences of Okay.

one coder now labels 10 of those as Accept and the remaining 15 as Ack, whereas the other labels the same 10 as Ack and the same 15 as Accept.


Siegel and Castellan's κ S&C is not affected by bias, whereas Cohen's κ Co is.

it is questionable whether the assumption of equal distributions underlying κ S&C is appropriate for coding in discourse and dialogue work.


The definition proposed by Grosz, Joshi, and Weinstein allows inferable entities, that is, entities that are not expressed at the surface level of the utterance or immediately recoverable from the subcategorization properties of the verb (as, for example, zero pronouns are) to constitute centers of an utterance.

the theory does not make explicit the parameters within which to characterize the class of permissible inferable elements or the constraints on doing so.


The drawback of this approach is that grammars are by their very nature application and domain specific.

automatic learning techniques may be adopted to learn from available examples.


The vocabulary of the new representation of the German part of the Verbmobil corpus, for example, in which full word forms are replaced by base form plus morphological and syntactic tags (lemmatag representation), is one and a half times as large as the vocabulary of the original corpus.

the information in the lemma-tag representation can be accessed gradually and ultimately reduced: For example, certain instances of words can be considered equivalent.


This is greater than the probability PT P of the pair una camera doppia/a room with two beds, 1.0 • 0.4 • 1.0 = 0.4.

the Viterbi score VT P for the first pair is 1.0 • 0.3 • 1.0 = 0.3, which is lower than the Viterbi score VT P for the second pair, 1.0 • 0.4 • 1.0 = 0.4.


It is well-known that a 1-gram entails a maximum generalization, allowing (extended) words to follow one another.

for sufficiently large m, a (nonsmoothed) m-gram is just an exact representation of the training strings (of extended words, in our case).


This could lead to addition of new, superflous strings, provided the states that to which we add transitions are reentrant/confluence.


Most of the speedup was not the result of using an algorithm optimized for sorted data-an improvement to the algorithm for adding strings in Carrasco and Forcada (2002) consisting in avoiding unnecessary cloning of prefix states (as described in section 3.2 and mentioned on page 215 in Carrasco and Forcada [2002] as a suggestion from one of Carrasco and Forcada's reviewers) was 3.12 and respectively 2.35 times faster than the original algorithm.


Specifically, if you already work with LEXC or XFST, this is the bible of the applications.

if you are interested in the mathematics of finite-state networks or in the computational aspects of their implementations, you are probably better off with a book such as Roche and Schabes (1997).


A minor note: The book is extremely well-written, its language is fluent and lucid, and hard as I tried, I could not find a single error or typo.

the repeated references to Xerox, the Xerox linguists, and the Xerox developers of the technology described in the book are exaggerated.


The Collins Parser is a fully supervised, history-based learner that models the parameters of the parser by taking statistics directly from the training data.

pLTIG's expectation-maximization-based induction algorithm is partially supervised; the model's parameters are estimated indirectly from the training data.


The baseline case requires an average of about 38,000 brackets in the training data.

to induce a grammar that reaches the same 80% parsing accuracy with the examples selected by f unc , the learner requires, on average, 19,000 training brackets.


Current extraction and retrieval technology focuses almost exclusively on the subject matter of documents.

additional aspects of a document influence its relevance, including evidential status and attitude (Kessler, Nunberg, Schütze 1997).


She uses machine learning to select among manually developed features.

the focus in our work is on automatically identifying features from the data.


Although Strong C1 is still not verified if we consider all 669 segments of text that contain NPs, the number of utterances that satisfy Strong C1 (264) is slightly larger than the number of those that don't (260).

identifying utterances with sentences also has several negative (if small) effects.


As shown in Table 12, With this instantiation (henceforth, IS) 390 utterances out of 669 (58.3%) satisfy Strong C1, and 177 (26.5%) violate it-significantly better than the instantiation with u=s and direct realization (henceforth, DS).

the number of utterances with more than one CB almost doubles again (and with respect to the DS instantiation), to 26 (3.9%) from 14 (2.1%).


As a result, the percentage of "continuous" transitions (Kibble 2001)-EST, CON, RET, SSH, and RSH-increases.

rSH and SSH increase, as well as EST and CON: In the IF instantiation with PrO2s, EST are the most common transition (20.2%), but in the IS instantiation, rSH are (18.4%).


The improvements are greater with the u=s instantiations, since with sentences it's more common for more than one CF to be realized in the same grammatical position; for example, in the IS instantiations in which PRO2s are considered as realizations of CFs, we find that 5.7% of utterances (38/669) have more than one CB.

strong C1 is already verified with these instantiations, even with simple grammatical function.


This claim is further supported by the evidence concerning the Repeated Name Penalty (Gordon, Grosz, and Gillion 1993).

the RNP is observed only in a subset of the cases that would be considered as CB mentions according to the definition provided by Constraint 3, and in the example we are discussing (10), neither Branicki nor the cupboard occur in (u229) in a position that would be subject to RNP effects according to Gordon et al.


we can show that any increase in the third term is more than offset in the labeling step.


In the current context, there is also justification for pursuing agreement on unlabeled data.

the Yarowsky algorithm does not assume the existence of two conditionally independent views of the data.


1 Of course coherence of a text depends on the realization of rhetorical relations (Mann and Thompson 1987) as well as referential continuity, and the latter is to an extent a byproduct of the former, as clauses that are rhetorically related also tend to mention the same entities.

even when a set of facts is arranged in a hierarchical RST structure, there are still many possible linear orderings with noticeable differences in referential coherence.


Karamanis (2001), Kibble (2001), and Beaver (2004), have argued for a ranking of the centering principles as opposed to weighting, and indeed Beaver provides a unified formulation of the centering rules and constraints as a ranked set of OT constraints.

we believe that such a ranking stands in need of empirical justification, and Beaver's data actually provide little evidence for strict ranking as opposed to weighting of constraints (see Kibble 2003).


For GL, a drastic improvement can be observed for all subclasses.

for very short words of BL, only a modest improvement is obtained.


For negative z, the two functions behave quite differently.

f ðzÞ ¼ e Àz shows an exponentially growing cost function as z Y À V. as z Y ÀV it can be seen that logð1 þ e Àz Þ Y logðe Àz Þ ¼ Àz, so this function shows asymptotically linear growth for negative z.


In the worst case, when every feature chosen appears on every training example, then C/ T = 1, and the two algorithms essentially have the same running time.

in sparse feature spaces there is reason to believe that C/ T will be small for most iterations.


First we recap the definition of the probability of a particular parse x i,q given parameter settingsā a: Pðx i,q j s i ,ā aÞ ¼ e Fðx i,q ,ā aÞ P j¼1 n i e Fðx i,j ,ā aÞ Recall that the log-loss is Unfortunately, unlike the case of ExpLoss, in general an analytic solution for BestWt does not exist.

we can define an iterative solution using techniques from iterative scaling (Della Pietra, Della Pietra, and Lafferty 1997).


Only those features which co-occur with k Ã on some example will need to have their values of BestWt and BestLoss updated.

this observation does not lead to an efficient algorithm: Updating these values is much more expensive than in the ExpLoss case.


Similarly to FrameNet, PropBank also attempts to label semantically related verbs consistently, relying primarily on VerbNet classes for determining semantic relatedness.

there is much less emphasis on the definition of the semantics of the class that the verbs are associated with, although for the relevant verbs additional semantic information is provided through the mapping to VerbNet.


The original treebank syntactic tree contains a trace which would allow one to recover this relation, coindexing the empty subject position of support with the noun phrase Big investment banks.

our automatic parser output does not include such traces.


Note that (12) contains some words (e.g., article and treaty) that do not actually occur with position bright, approveÀ in the corpus.

as these words actually occur with most of the positions that are similar to bright, approveÀ, we may assume that they satisfy the condition of this particular position.


In (15), a single set of words is associated with the two positions, since the positions have in common the same semantic condition (or selection restrictions).

the scope of the condition is still too narrow: It merely embraces two positions.


Words are weighted considering their dispersion (global weight) and their conditional probability given a position (local weight).

the weight Assoc, measuring the degree of association between word w and position p, is computed by equation 16: the conditional probability P MLE is estimated by using the maximum likelihood estimate (MLE), which is calculated in (17): where f ðp, wÞ represents the frequency of word w appearing in position p, and FðpÞ is defined, for a particular position, as the total sum of its word frequencies:~i f ðp, w i Þ.


Learned clusters represent linguistic requirements that cannot be reduced to a smaller set of general syntactico-semantic roles, such as Agent, Patient, Theme, and Instrument.

they cannot be associated with word-specific roles like, for instance, Reader, Eater, and Singer.


They are situated, in fact, at the domain-specific level, which is considered more appropriate for use in computational tasks (Gildea and Jurafsky 2002).

given the too-restrictive constraints of the algorithm, the clustering method also overgenerates redundant clusters.


Similarly to our approach, these methods follow both the relative view on word similarity and the assumption on contextual word sense, which were introduced above in sections 3.3 and 3.4, respectively.

these methods differ from ours in several aspects.


As in Clustering 1 (section 7.1), each basic cluster selects only those words occurring in all positions of the cluster.

allegrini, Montemagni, and Pirrelli (2000) define a second clustering step involving significant differences with regard to our Clustering 2.


These methods, then, follow both the absolute view on word similarity and Harris's distributional hypothesis, which we introduced in section 2.3.

in order to make the absolute view more relative, a collection of small and tight clusters (called committees) is proposed in a first step.


On the one hand, the words occurring in p are called the cohorts of w. The cohorts are similar to w only with regard to position p (relativized view).

a corpus-based thesaurus is used to select words similar to w with regard to its whole position distribution (absolute view).


There are no significant differences between the scores obtained from corpus EC and those from PGR, CR, for instance, obtains very similar F-scores over the two corpora.

there are important differences among the precision values associated with the three phrase sequences.


Clearly, the study of schema-based argumentation is a worthwhile endeavor.

it would have been better if the book had, at least in part, considered a combination of these approaches, rather than swinging completely to the ''rhetorical approach only'' side.


I would recommend this book to graduate students or researchers who intend to work in argumentation.

at the same time, I would suggest reading the book critically and complementing it with material about quantitative and probabilistic methods and user modeling.


One of the first proposals made was an analysis of German scrambling data using nonlocal MCTAG with additional dominance constraints (Becker, Joshi, and Rambow 1991).

the formal properties of nonlocal MCTAG are not well understood, and it is assumed that the formalism is not polynomially parsable.


Whether this grammar is considered to be a general RSN-MCTAG or an RSN-MCTAG of arity four does not matter in this case, since even in the general case, all possible SN-derivation structures are of arity four.

in the case of other RSN-MCTAGs, the restriction to a certain arity might exclude certain TAG derivation trees and thereby decrease the language generated by the grammar.


Finally, we were surprised that the number of concepts per word was almost identical for the five English dictionaries tested (see Table 2).

we found that this was not always the case, since further trials on six other English dictionaries gave a larger range of values, shown in Table 4, varying from 1.23 to 1.55.


Furthermore, accounts of discourse structure vary greatly with respect to how many discourse relations they assume, ranging from 2 (Grosz and Sidner 1986) to over 400 different coherence relations (reported in Hovy and Maier [1995]).

hovy and Maier (1995) argue that, at least for informationallevel accounts, taxonomies with more relations represent subtypes of taxonomies with fewer relations.


to cause-effect relations, there is no causal relation between the events described by the two discourse segments.


Structural discourse relations are represented within a lexicalized tree-adjoining grammar framework, and the resultant structural discourse structure is represented by a tree.


Note furthermore that the RST tree contains an evaluation-s relation between 6 and 1-5.

this evaluation-s relation seems to hold rather between 6 and 3-4: What is being evaluated is a chance for the Contras to win Table 6 Comparison for (26) of tree-based RST structure (from Carlson, Marcu, and Okurowski (2002) and our chain-graph-based structure.


the discussion of Knott [1996] in section 3).

there is a possible alternative hypothesis to Knott's (1996).


In sum, the statistical results on nodes with multiple parents suggest that they are a frequent phenomenon and that they are not limited to certain kinds of coherence relations.

as with crossed dependencies, removing certain kinds of coherence relations (elaboration and similarity) can reduce the mean in-degree of nodes and the proportion of nodes with in-degree greater than one.


Since Jean Carletta (1996) exposed computational linguists to the desirability of using chance-corrected agreement statistics to infer the reliability of data generated by applying coding schemes, there has been a general acceptance of their use within the field.

there are prevailing misunderstandings concerning agreement statistics and the meaning of reliability.


The application of agreement statistics has done much to improve the scientific rigor of discourse and dialogue research.

unless we understand what we are attempting to prove and which tests are appropriate, the results of evaluation can be unsatisfactory or, worse still, misleading.


On the one hand, the presence of numerous sources conveying the same information causes difficulties for end users of search engines and news providers; they must read the same information over and over again.

redundancy can be exploited to identify important and accurate information for applications such as summarization and question answering (Mani and Bloedorn 1997;Radev and McKeown 1998;Radev, Prager, and Samn 2000;Clarke, Cormack, and Lynam 2001;Dumais et al.


At one extreme, we might consider a shallow approach to the fusion problem, adapting the "bag of words" approach.

sentence intersection in a set-theoretic sense produces poor results.


At the other extreme, previous approaches (Radev and McKeown 1998) have demonstrated that this task is feasible when a detailed semantic representation of the input sentences is available.

these approaches operate in a limited domain (e.g., terrorist events), where information extraction systems can be used to interpret the source text.


This algorithm has several features in common with our method: It operates over syntactic dependency representations and employs recursive computation to find an optimal solution.

our method is different in two key aspects.


In this article we present an approach to automating the process of lexical acquisition for LFG (i.e., grammatical-function-based systems).

our approach also generalizes to CFG category-based approaches.


Dalrymple (2001) argues that there are cases, albeit exceptional ones, in which constraints on syntactic category are an issue in subcategorization.

to much of the work reviewed in Section 3, which limits itself to the extraction of surface syntactic subcategorization details, our system can provide this information as well as details of grammatical function.


They use, for example, a relatively coarse-grained notion of semantic compatibility between a few high-level concepts in WordNet (Soon, Ng, and Lim 2001), or more detailed hyponymy and synonymy links between anaphor and antecedent head nouns (Vieira and Poesio 2000;Harabagiu, Bunescu, and Maiorano 2001;Ng and Cardie 2002b, among others).

several researchers have pointed out that the incorporated information is still insufficient.


Thus, the desired link might be derived through the analysis of the gloss together with derivation of periodical from periodic.

such extensive mining of the ontology (as performed, e.g., by Harabagiu, Bunescu, and Maiorano [2001]) can be costly.


9 Using all senses of anaphor and potential antecedents in the search for relations might yield a link between an incorrect antecedent candidate and the anaphor due to an inappropriate sense selection.

considering only the most frequent sense for anaphor and antecedent (as is done in Soon, Ng, and Lim [2001]) might lead to wrong antecedent assignment if a minority sense is intended in the text.


We used a t-test to measure the difference between two algorithms in the proportion of correctly resolved anaphors.

there are many examples which are easy (for example, string-matching examples) and that therefore most or all algorithms will resolve correctly, as well as many that are too hard for all algorithms.


String matching is quite successful for coreference (baselineSTR v2n covers nearly 70% of the cases with a precision of 80.9%).

because algoWeb v4n never over- rules string matching, the errors of baselineSTR v2n are preserved here and account for 24.1% of all mistakes.


I gave the paper and stimulated some laughter.

sitting in the audience were Margaret Masterman and Frederick Parker Rhodes.


I was precocious, but very junior, so that my role was no more than that of a fly on the wall.

i was indeed present at a meeting in the office of David Hays at Rand when the name "computational linguistics" was settled upon.


In other applications, distributional similarity is taken to be an approximation to semantic similarity.

due to the wide range of potential applications and the lack of a strict definition of the concept of distributional similarity, many methods of calculating distributional similarity have been proposed or adopted.


Hindle and Rooth (1993) note that the correct decision depends on all four lexical events (the verb, the object, the preposition, and the prepositional object).

a statistical model built on the basis of four lexical events must cope with extremely sparse data.


For example, a cottage is a type of building and a brother is a type of person, and so the co-occurrence of any type of building and any type of person might increase the probability that the PP in example (1) attaches to the verb.

it is unclear whether the classes over which probability distributions are induced need to be semantic or whether they could be purely distributional.


In order to answer these questions, we take a pragmatic, application-oriented approach to evaluation that is based on the assumption that we want to know which words are distributionally similar because particular applications can make use of this information.

high performance in one application area is not necessarily correlated with high performance in another application area (Weeds and Weir 2003a).


However, in applications, it is normally necessary to compute a single number in order to determine neighborhood or cluster membership.

the classic way to combine precision and recall in IR is to compute the F-score; that is, the harmonic mean of precision and recall: we do not wish to assume that a good substitute requires both high precision and high recall of the target distribution.


This restriction was for computational efficiency and to avoid computing similarities based on the potentially unreliable descriptions of very lowfrequency words.

since our evaluation is comparative, we do not expect our results to be affected by this or any of the other restrictions.


It is computed as twice the ratio between the size of the intersection of the two feature sets and the sum of the sizes of the individual feature sets: where F(w) = {c : P(c|w) > 0} 29According to this measure, the similarity between words with no shared features is zero and the similarity between words with identical feature sets is 1.

as shown below, this formula is equivalent to a special case in the CR framework: the harmonic mean of precision and recall (or F-score) using the additive type-based CRM.


The first of these approaches is taken by Curran and Moens (2002), who evaluate a number of different distributional similarity measures and weight functions against a gold standard thesaurus compiled from Roget's, the Macquarie thesaurus, and the Moby thesaurus.

we argue that this approach can only be considered when distributional similarity is required as an approximation to semantic similarity and that, in any case, it is not ideal since it is not clear that there is a single "right answer" as to which words are most distributionally similar.


An underlying assumption of this approach is that WordNet is a gold standard for semantic similarity, which, as is discussed by Weeds (2003), is unrealistic.

it seems reasonable to suppose that a distributional similarity measure that more closely predicts a semantic measure based on WordNet is more likely to be a good predictor of semantic similarity.


Third, all of the measures perform significantly better for high-frequency nouns than for low-frequency nouns.

some of the measures (sim lin , sim jacc and sim dice ) perform considerably worse for low-frequency nouns.


more neighbors increases performance, since more neighbors allow decisions to be made in a greater number of cases.

when k increases beyond an optimal value, a greater number of these decisions will be in the wrong direction, since these words are not very similar to the target word, leading to a decrease in performance.


The low values of γ indicate that a combination of precision and recall that is closer to a weighted arithmetic mean is generally better than one that is closer to an unweighted harmonic mean.

this does not hold for the t-test based CRMs for low-frequency nouns.


If we don't apply the filter before the classifier, the recall results increase by about 20% (with no loss in precision).

the filter plays a very important role in keeping the extraction pipeline robust and efficient (as shown in Figure 7, the filter discards 99% of the candidate pairs), so this loss of recall is a price worth paying.


Two sentences may share many content words and yet express different meanings (see Figure 14, example 1).

our task of getting useful MT training data does not require a perfect solution; as we have seen, even such noisy training pairs can help improve a translation system's performance.


From the standpoint of the relative jump models, jumping over the four words tripled it 's sales and jumping over the four words of Apple Macintosh systems are exactly the same.

7 intuitively, we would be much more willing to jump over the latter than the former.


Under ML estimation, we will simply insert an entry in the t-table for the entire summary for some uncommon or unique document word and are done.

a priori we do not believe that such a parameter is likely.


Many current segmenters simply ignore NWs, assuming that they are of little significance in most applications.

we argue that the identification of those words is critical because a single unidentified word can cause segmentation errors in the surrounding words.


As described earlier, we argue that Chinese words (or segmentation units) cannot be defined independently of the applications, and hence a more flexible system (i.e., an adaptive segmenter such as MSRSeg) should be adopted.

we are faced with the challenge of performing an objective and rigorous evaluation of such a system.


The real evaluation will require some application data sets (i.e., segmented texts used by different applications).

such application data are not available yet, and no other system has undergone such evaluation, so there is no way to compare our system against others in this fashion.